Related work

The foundational work on continual learning, 1989 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

12 papers of 5,456Sort Recent · Most cited
  1. 2020
    Continual Learning with Bayesian Neural Networks for Non-Stationary DataRichard Kurle, Botond Cseke, Alexej Klushyn … Stephan GünnemannICLR
  2. 2020
  3. 2020
    BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong LearningYeming Wen, Dustin Tran, Jimmy BaICLR · University of Toronto · Google (United States)
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  4. 2020
    A Neural Dirichlet Process Mixture Model for Task-Free Continual LearningSoochan Lee, Junsoo Ha, Dongsu Zhang, Gunhee KimICLR
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  5. 2020
  6. 2020
    Progressive Memory Banks for Incremental Domain AdaptationNabiha Asghar, Lili Mou, Kira A. Selby … Xin JiangICLR
  7. 2020
    Continual Learning with Adaptive Weights (CLAW)Tameem Adel, Han Zhao, Richard E. TurnerICLR
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  8. 2020
    Compositional Language Continual LearningYuanpeng Li, Liang Zhao, Kenneth Ward Church, Mohamed ElhoseinyICLR
  9. 2020
    LAMOL: LAnguage MOdeling for Lifelong Language LearningFan-Keng Sun, Cheng-Hao Ho, Hung-yi LeeICLR · Massachusetts Institute of Technology · National Taiwan University
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  10. 2020
    Uncertainty-guided Continual Learning with Bayesian Neural NetworksSayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus RohrbachICLR · University of California, Berkeley · King Abdullah University of Science and Technology · +1
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  11. 2020
    Continual learning with hypernetworksJohannes von Oswald, Christian Henning, J. Sacramento, B. GreweICLR
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  12. 2020
    Scalable and Order-robust Continual Learning with Additive Parameter DecompositionJaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju HwangICLR · Korea Advanced Institute of Science and Technology
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.